MDPI - Publisher of Open Access Journals

21 pages, 12038 KiB

Open AccessTechnical Note

Image-to-Image Translation-Based Deep Learning Application for Object Identification in Industrial Robot Systems

by Timotei István Erdei, Tibor Péter Kapusi, András Hajdu and Géza Husi

Robotics 2024, 13(6), 88; https://doi.org/10.3390/robotics13060088 - 2 Jun 2024

Cited by 4 | Viewed by 2841

Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset. Full article

(This article belongs to the Topic Smart Production in Terms of Industry 4.0 and 5.0)

► Show Figures

Figure 1

25 pages, 8862 KiB

Open AccessArticle

Design of a Digital Twin Training Centre for an Industrial Robot Arm

by Timotei István Erdei, Rudolf Krakó and Géza Husi

Appl. Sci. 2022, 12(17), 8862; https://doi.org/10.3390/app12178862 - 3 Sep 2022

Cited by 26 | Viewed by 7993

Abstract

The Cyber-Physical and Intelligent Robotics Laboratory has been digitally recreated, and it includes all the key elements that allow 6-axis industrial robots to perform PTP, LIN, and CIRC motions. Furthermore, the user can create a program with these motion types. The human–machine interface is also integrated into our system. It can also assist SMEs in developing their in-house training. After all, training on an industrial robot unit does not entail installation costs within the facility. Nor are there any maintenance and servicing costs. Since the lab is digital, additional robot units can be added or removed. Thus, areas for training or production can be pre-configured within each facility. Because of the customizability and virtual education format, there is no room capacity problem, and trainees can participate in the exercises in parallel. Exercises were also conducted to evaluate the program’s impact on teaching, and the results showed that using machine units can improve teaching. Even today’s digital labs cannot physically convey the sense of space or the relative weights of different elements in virtual space. Even with these features, individuals can operate a machine more effectively than relying solely on traditional, non-interactive demonstration materials. Full article

(This article belongs to the Topic Virtual Reality, Digital Twins, the Metaverse)

► Show Figures

Figure 1

20 pages, 5667 KiB

Open AccessArticle

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

by Tibor Péter Kapusi, Timotei István Erdei, Géza Husi and András Hajdu

Robotics 2022, 11(4), 69; https://doi.org/10.3390/robotics11040069 - 30 Jun 2022

Cited by 26 | Viewed by 5087

Abstract

In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed. Full article

(This article belongs to the Topic New Frontiers in Industry 4.0)

► Show Figures

Figure 1

Search Results (3)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (3)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI